Google Cloud: 52% of Executives Say Their Organizations Have Deployed AI Agents

Enterprises experience consistent year-over-year revenue growth from their generative AI initiatives and steady investment into AI and agentic projects, according to a new study from Google Cloud. 

The report also highlighted a new group of "agentic AI early adopters" whose organizations are deploying agents at scale and seeing higher rates of ROI from agentic AI in areas like customer service and experience, marketing, security operations and cybersecurity, and software development.

The Rise of AI Agents

The study revealed that AI agents — specialized large language models (LLMs) that can independently plan, reason, and perform tasks — are rapidly being adopted in organizations.

Key findings include:

  • Agents proliferating fast: More than half (52%) of executives report their organization is actively using AI agents, with 39% reporting their company has launched more than ten.
  • The early adopter advantage: A distinct group of "agentic AI early adopters," representing 13% of executives surveyed, indicate their organizations are dedicating at least 50% of their future AI budget to AI agents and already have agents deeply embedded across operations. 88% of these leaders report their organizations are seeing ROI from generative AI on at least one use case, compared to a 74% average across all organizations.
  • Higher rates of ROI: These early adopters are consistently more likely to report seeing ROI on agentic AI use cases. These include enhancing customer service and experience (43% vs. 36% average), boosting marketing effectiveness (41% vs. 33% average), strengthening security operations (40% vs. 30% average), and improved software development (37% vs 27% average).

"This year's research shows we're entering the next chapter of the AI wave. The conversation has moved from 'if' to 'how fast,' and the new differentiator is agentic AI," said Oliver Parker, VP, Global Generative AI Go-To-Market, Google Cloud. "Early adopters of agents are not just automating tasks; they are also redesigning core business processes. By championing AI as a core engine for competitive growth and thus securing dedicated budgets, they are providing a clear roadmap for any organization looking to scale, solve complex challenges, and achieve more consistent ROI."

Applications of Agentic AI

The study also highlighted diverse application of AI agents across various industries and regions:

  • Use cases span departments: The most common cross-industry applications for AI agents reported in the study were customer service and experience (49%), marketing (46%), security operations and cybersecurity (46%), and tech support (45%).
  • Leading vs. lagging industries: Adoption of agentic AI is consistent across most industries, with Healthcare & Life Sciences slightly lagging. Industry-specific use cases include fraud management and detection in financial services (43%); quality control in retail and CPG (39%); and network or equipment configuration and automation in telco (39%).
  • Regional nuances are pronounced: According to the study, use case priorities differed by region. Executives in Europe, for example, report AI-enhanced tech support as the top AI agent use case, while executives in Japan-Asia Pacific (JAPAC) report the top use case is focused on customer service and in Latin America on marketing.

"We're seeing organizations around the world use agentic AI to tackle complex industry-specific tasks — from fraud detection in financial services to quality control in retail," said Carrie Tharp, VP, Head of Strategic Industries and Solutions, Google Cloud. "This isn't just about efficiency; it's about embedding intelligence directly into the business."

ROI and Investment Remains Strong, As Focus Shifts to Privacy and Security

Financial returns on generative AI remain consistent with last year's findings: 74% of executives report achieving ROI within the first year. Furthermore, over half of executives (56%) say generative AI has led to business growth. Of those, 71% report an increase in revenue, with 53% of that group estimating gains of 6-10%.

The top drivers of generative AI value-add in the study were productivity (70%), customer experience (63%), and business growth (56%), and the data also showed an increase among organizations that are taking an AI application from idea to use case in production within 3-6 months (51% in 2025, vs. 47% in 2024).

As investments in generative AI grow — with 77% of executives in the study reporting their organization has increased spending on gen AI as technology costs fall and 48% reallocating non-AI budgets toward gen AI — a new set of challenges is emerging. 37% of respondents reported data privacy and security as among their organization's top three LLM provider considerations, followed by integration with existing systems and cost. This suggests organizations are considering key enterprise needs before evaluating more advanced or differentiated capabilities like specific features or customization.

"2024 proved that generative AI works; 2025 is all about compounding that success," added Parker. "The biggest hurdles for most organizations are rooted in foundational data security and systems integration. The solution is to adopt a modern data strategy with strong governance from the start."

Methodology: The comprehensive ROI of AI Study, commissioned by Google Cloud and conducted by National Research Group, surveyed 3,466 senior leaders of global enterprises across 24 countries with generative AI deployment within their organizations.

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Google Cloud: 52% of Executives Say Their Organizations Have Deployed AI Agents

Enterprises experience consistent year-over-year revenue growth from their generative AI initiatives and steady investment into AI and agentic projects, according to a new study from Google Cloud. 

The report also highlighted a new group of "agentic AI early adopters" whose organizations are deploying agents at scale and seeing higher rates of ROI from agentic AI in areas like customer service and experience, marketing, security operations and cybersecurity, and software development.

The Rise of AI Agents

The study revealed that AI agents — specialized large language models (LLMs) that can independently plan, reason, and perform tasks — are rapidly being adopted in organizations.

Key findings include:

  • Agents proliferating fast: More than half (52%) of executives report their organization is actively using AI agents, with 39% reporting their company has launched more than ten.
  • The early adopter advantage: A distinct group of "agentic AI early adopters," representing 13% of executives surveyed, indicate their organizations are dedicating at least 50% of their future AI budget to AI agents and already have agents deeply embedded across operations. 88% of these leaders report their organizations are seeing ROI from generative AI on at least one use case, compared to a 74% average across all organizations.
  • Higher rates of ROI: These early adopters are consistently more likely to report seeing ROI on agentic AI use cases. These include enhancing customer service and experience (43% vs. 36% average), boosting marketing effectiveness (41% vs. 33% average), strengthening security operations (40% vs. 30% average), and improved software development (37% vs 27% average).

"This year's research shows we're entering the next chapter of the AI wave. The conversation has moved from 'if' to 'how fast,' and the new differentiator is agentic AI," said Oliver Parker, VP, Global Generative AI Go-To-Market, Google Cloud. "Early adopters of agents are not just automating tasks; they are also redesigning core business processes. By championing AI as a core engine for competitive growth and thus securing dedicated budgets, they are providing a clear roadmap for any organization looking to scale, solve complex challenges, and achieve more consistent ROI."

Applications of Agentic AI

The study also highlighted diverse application of AI agents across various industries and regions:

  • Use cases span departments: The most common cross-industry applications for AI agents reported in the study were customer service and experience (49%), marketing (46%), security operations and cybersecurity (46%), and tech support (45%).
  • Leading vs. lagging industries: Adoption of agentic AI is consistent across most industries, with Healthcare & Life Sciences slightly lagging. Industry-specific use cases include fraud management and detection in financial services (43%); quality control in retail and CPG (39%); and network or equipment configuration and automation in telco (39%).
  • Regional nuances are pronounced: According to the study, use case priorities differed by region. Executives in Europe, for example, report AI-enhanced tech support as the top AI agent use case, while executives in Japan-Asia Pacific (JAPAC) report the top use case is focused on customer service and in Latin America on marketing.

"We're seeing organizations around the world use agentic AI to tackle complex industry-specific tasks — from fraud detection in financial services to quality control in retail," said Carrie Tharp, VP, Head of Strategic Industries and Solutions, Google Cloud. "This isn't just about efficiency; it's about embedding intelligence directly into the business."

ROI and Investment Remains Strong, As Focus Shifts to Privacy and Security

Financial returns on generative AI remain consistent with last year's findings: 74% of executives report achieving ROI within the first year. Furthermore, over half of executives (56%) say generative AI has led to business growth. Of those, 71% report an increase in revenue, with 53% of that group estimating gains of 6-10%.

The top drivers of generative AI value-add in the study were productivity (70%), customer experience (63%), and business growth (56%), and the data also showed an increase among organizations that are taking an AI application from idea to use case in production within 3-6 months (51% in 2025, vs. 47% in 2024).

As investments in generative AI grow — with 77% of executives in the study reporting their organization has increased spending on gen AI as technology costs fall and 48% reallocating non-AI budgets toward gen AI — a new set of challenges is emerging. 37% of respondents reported data privacy and security as among their organization's top three LLM provider considerations, followed by integration with existing systems and cost. This suggests organizations are considering key enterprise needs before evaluating more advanced or differentiated capabilities like specific features or customization.

"2024 proved that generative AI works; 2025 is all about compounding that success," added Parker. "The biggest hurdles for most organizations are rooted in foundational data security and systems integration. The solution is to adopt a modern data strategy with strong governance from the start."

Methodology: The comprehensive ROI of AI Study, commissioned by Google Cloud and conducted by National Research Group, surveyed 3,466 senior leaders of global enterprises across 24 countries with generative AI deployment within their organizations.

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The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

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Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

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The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

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Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

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If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

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